{
  "abstract": "The increasing complexity and volume of network traffic in modern digital infrastructures have rendered traditional rule-based intrusion detection systems insufficient for identifying sophisticated and evolving cyber threats. This study proposes an attention-based intrusion detection framework leveraging Transformer networks to model complex dependencies within network traffic data. Using the UNSW-NB15 dataset, a widely recognized benchmark for network intrusion detection, the proposed model captures both local and global feature interactions through self-attention mechanisms, enabling effective discrimination between normal and malicious activities. The experimental setup focuses on a supervised learning paradigm, where network traffic is preprocessed, encoded, and evaluated under a clearly defined classification task. The Transformer model achieved an accuracy of 96.8%, with strong performance across precision, recall, F1-score, and AUC-ROC metrics, demonstrating its robustness in handling high-dimensional and heterogeneous cybersecurity data. To ensure methodological rigor and reproducibility, the study employs a transparent data preprocessing pipeline, strict train-test separation, and detailed reporting of model configuration and evaluation procedures. Unlike prior works that rely heavily on convolutional or recurrent architectures, this research highlights the effectiveness of attention-based models for scalable and context-aware intrusion detection. While the current work focuses on detection, it outlines a conceptual pathway for integrating decision-making mechanisms to support adaptive and automated response in future systems. The findings contribute to the advancement of intelligent cybersecurity by demonstrating that Transformer-based architectures can provide reliable and generalizable performance for real-world intrusion detection tasks.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, University of Benin",
    "College of Business, Westcliff University",
    "College of Business, Engineering and Technology, National University",
    "Department Computer Science, Veritas University",
    "Department of Data Science, University of Benin",
    "Department of Computer and Information Science, University of the Cumberlands"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Godfrey Perfectson Oise"
    },
    {
      "affs": [
        1
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      "name": "Evans Mintah"
    },
    {
      "affs": [
        2
      ],
      "name": "Oludare Sokoya"
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    {
      "affs": [
        3
      ],
      "name": "Felix Oshiorenoya Uloko"
    },
    {
      "affs": [
        1
      ],
      "name": "Tejiri Jessa"
    },
    {
      "affs": [
        4
      ],
      "name": "Susan Konyeha"
    },
    {
      "affs": [
        5
      ],
      "name": "Osahon Ukpebor"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2zv0gh7zreqhy.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-65710-2",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-65710-2",
  "id": "2zv0gh7zreqhy",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-65710-2",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1705|Network Security and Intrusion Detection"
    ]
  ],
  "openalex": "W7201986029",
  "pdf_url": "https://www.nature.com/articles/s41598-026-65710-2_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/2zv0gh7zreqhy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:54Z",
      "native_id": "ccb276ae11bc47ccab1ec11c5c15a582",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-65710-2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Attention-based intrusion detection using transformer networks on UNSW-NB15",
  "title_text": "Attention-based intrusion detection using transformer networks on UNSW-NB15",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:54Z",
  "url": "https://research.draconitic.com/paper/2zv0gh7zreqhy",
  "venue": "Scientific Reports",
  "versions": []
}